Durum wheat yield forecasting using machine learning

Chergui, Nabila

doi:10.1016/j.aiia.2022.09.003

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…Following was the performance of k-NN. Our results are in accordance with Bebie et al [78] and Chergui [79], who observed the best performance by RF and k-NN models for the durum wheat grain yield prediction cultivated in a Mediterranean environment, and Yue et al [73] and Zhou et al [67], who observed better performance from the RF and SVM models for wheat biomass prediction.…”

Section: Machine Learning (Ml) Approaches For Grain Yield Estimationsupporting

confidence: 92%

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

Badagliacca,

Messina,

Praticò

et al. 2023

AgriEngineering

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Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson’s correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson’s correlations between VIs and grain yield were good (R2 > 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson’s correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models.

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Section: Machine Learning (Ml) Approaches For Grain Yield Estimationsupporting

confidence: 92%

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

Badagliacca,

Messina,

Praticò

et al. 2023

AgriEngineering

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“…Enhanced wheat yield predictions in two Algerian provinces through data augmentation, with a Deep Neural Network leading in one (RMSE of 4 kg/ha) and Random Forest in another (RMSE of 5 kg/ha). [28] Recently, Neural Network-based crop yield prediction models have also demonstrated exceptional accuracy [29][30][31][32][33][34].…”

Section: Paudel Et Al Machine Learning-based Methodsmentioning

confidence: 99%

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

Li,

Nie,

2024

Agronomy

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One of the crucial research areas in agricultural decision-making processes is crop yield prediction. This study leverages the advantages of hybrid models to address the complex interplay of genetic, environmental, and management factors to achieve more accurate crop yield forecasts. Therefore, this study used the data of wheat growth environment, crop management, and historical yield in experimental fields in Anding District, Dingxi City, Gansu Province from 1984 to 2021 to construct eight machine learning models and ensemble models. Furthermore, Agricultural Production Systems sIMulator (APSIM), machine learning (ML), and APSIM combined with machine learning (APSIM-ML) were employed to predict wheat yields in 2012, 2016, and 2021. The results show that the APSIM-ML weighted ensemble prediction model, optimized to minimize the MSE, performed the best. Compared to the optimized ML and APSIM models, the average improvements in the RMSE, RRMSE, and MBE for the test years were 43.54 kg/ha, 3.55%, and 15.54 kg/ha, and 93.96 kg/ha, 7.55%, and 104.21 kg/ha, respectively. At the same time, we found that the dynamic flow of water and nitrogen between the soil and crops had the greatest impact on wheat yield prediction. This study improved the accuracy of dryland wheat yield prediction in Gansu Province and provides technical support for the intelligent production of dryland wheat in the loess hilly area.

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“…For example, Enkvetchakul and Surinta [15] developed a plant disease recognition system using deep convolutional neural networks, achieving higher accuracy by combining offline training with data augmentation techniques. Chergui [16] evaluated five regression models using three datasets (primary, with additional features, and augmented), finding that cross-validation showed an overall performance increase with augmented data.…”

Section: Introductionmentioning

confidence: 99%

Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars

Montesinos-López,

Sivakumar,

Huerta Prado

et al. 2024

Algorithms

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Genomic selection (GS) is a groundbreaking statistical machine learning method for advancing plant and animal breeding. Nonetheless, its practical implementation remains challenging due to numerous factors affecting its predictive performance. This research explores the potential of data augmentation to enhance prediction accuracy across entire datasets and specifically within the top 20% of the testing set. Our findings indicate that, overall, the data augmentation method (method A), when compared to the conventional model (method C) and assessed using Mean Arctangent Absolute Prediction Error (MAAPE) and normalized root mean square error (NRMSE), did not improve the prediction accuracy for the unobserved cultivars. However, significant improvements in prediction accuracy (evidenced by reduced prediction error) were observed when data augmentation was applied exclusively to the top 20% of the testing set. Specifically, reductions in MAAPE_20 and NRMSE_20 by 52.86% and 41.05%, respectively, were noted across various datasets. Further investigation is needed to refine data augmentation techniques for effective use in genomic prediction.

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Durum wheat yield forecasting using machine learning

Cited by 4 publications

References 38 publications

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

Multispectral Vegetation Indices and Machine Learning Approaches for Durum Wheat (Triticum durum Desf.) Yield Prediction across Different Varieties

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars

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